Let A be a matrix $n\times n$, given a n-vector $v$, what conditions over $v$ and $A$ are necessaries for $[v, Av,..., A^{n-1}v]$ will be linearly independent?
For example if $v$ is a eigenvector or $A^k=Id$ $(k<n)$, they are not linearly independent.
In other words, when the orbit of a matrix over a vector space $V$ (finite dimensional) can generates a basis of $V$?
A matrix (associated to an endomorphism) for which it exists $v$ such that $(v,Av, \dots, A^{n-1})$ is linearly independent is called a cyclic endomorphism. This is a direct French translation and I'm not sure that this is a proper English mathematical wording.
At least Wikipedia mentions that a vector $v$ for which $(v,Av, \dots, A^{n-1})$ spans $V$ is a cyclic vector.
You can have a look to French Wikipedia Decomposition de Frobenius, in particular the paragraph Endomorphisme cyclique. Unfortunately, the English version seems to lack a similar paragraph.
This paragraph mentions equivalent conditions for an endomorphism $u$ to be cyclic:
the degree of the minimum polynomial of $u$ is equal to the dimension of $V$,
the minimal polynomial and the characteristic polynomial of $u$ are equal (with the sign near);
an endomorphism commutes with $u$ (if and) only if it is a polynomial in $u$;
there is a base of $V$ in which the matrix of $u$ is a companion matrix. It is then the companion matrix of the minimal polynomial of $u$.
This is basically a restatement of the problem, but it still might be useful.
For every vector $v \in V$ there exists a unique monic polynomial $p$ of minimal degree such that $p(A)v = 0$.
Namely, the minimal polynomial $m_A$ of $A$ annihilates $A$ so the set of all monic polynomials $q$ such that $q(A)v = 0$ is nonempty. Consider the polynomials of minimal degree.
Let $p,q$ be two such polynomials. Then $(q-r)(A)v = q(A)v - r(A)v = 0$ and $q-r$ is of lesser degree than the minimal, which implies $q = r$.
As a consequence, if $q(A)v= 0$ then $p \,|\,q$. Namely, $\deg q \ge \deg p$ so there exist unique $g, h$ such that $p = qg + h$ with $\deg h < \deg p$. In particular
$$0 = q(A)v = g(A)p(A)v + h(A)v = h(A)v \implies h = 0 \implies p \,|\,q$$
Note that $\deg p \le n$ because $\deg m_A \le n$ and $p \,|\, m_A$.
Therefore, linear independence of $\{v, Av, \ldots, A^{n-1}v\}$ is equivalent to the fact that $\deg p = n$, which in turn is equivalent to $m_A = p$.
The answer to your question "when $(*)$ the orbit $\{v,Av,\cdots,A^{n-1}v\}$ over a vector space $V$ can generate a basis of $V$ ?" is, in a probabilistic point of view: ALWAYS.
Indeed, let $\chi_A$ be the characteristic polynomial of $A$ and $discr_A$ be its discriminant. The set $Z$ of $A\in M_n(\mathbb{C})$ s.t. $discr_A\not= 0$ is Zariski open dense. Thus, if the entries of $A$ are randomly chosen (using a normal law for example) then $A\in Z$ (that is $A$ has $n$ distinct eigenvalues) with probability $1$. We may assume that $A$ is diagonal; then the vector $v=[1,\cdots,1]^T$ realizes $(*)$ and more generally, a randomly chosen vector realizes $(*)$ with probability $1$; therefore, when $A\in Z$ (not in a diagonal form) then a random vector realizes $(*)$ with probability $1$.
Of course, there exist matrices, with multiple eigenvalues, that have the considered property. cf. the other answers.
Some context: the orbit you are describing is called a Krylov Subspace.
For the case where $ V = \mathbb{R}^n$:
Consider a diagonalizable $A$: since the eigenvectors of $A$ span $\mathbb{R}^n$, we can write
$$ v = \sum_i^n c_i \mathbf{u}_i, \; \mathbf{u}_i \text{ eigenvector of $A$} $$
Then, for every power of $A$, we have
$$ A^k v = A^k \left( \sum_i c_i \mathbf{u}_i \right) = \sum_i c_i A^k \mathbf{u}_i = \sum_i c_i (\lambda_i)^k \mathbf{u}_i $$
Based on the above observation, we may write $\left\{ v, Av, \dots, A^{n-1} v \right\}$ in compact form as
$$ \{ v, Av, \dots, A^{n-1}v\} = \underbrace{\begin{pmatrix} c_1 \mathbf{u}_1 & \dots & c_n \mathbf{u}_n \end{pmatrix}}_{\displaystyle=: U_c, \in \mathbb{R}^{n \times n}} \underbrace{\begin{pmatrix} 1 & \lambda_1 & \dots & \lambda_1^{n-1} \\ 1 & \dots & \dots & \dots \\ 1 & \lambda_n & \dots & \lambda_n^{n-1} \end{pmatrix}}_{\Lambda} $$
In order for the above to be a basis of $V$, we want its determinant to be nonzero, which means that we want $$ \det(\Lambda) \neq 0 \Rightarrow \prod_{i \neq j} (\lambda_i - \lambda_j) \neq 0 \Leftrightarrow \lambda_i \neq \lambda_j, i \neq j, \; \\ \det(U_c) \neq 0 \Rightarrow c_i \neq 0, \; \forall i $$ where the first equality follows from the fact that $\Lambda$ is a Vandermonde Matrix. If $v$ is representable as a linear combination of $d < n$ eigenvectors, one of the $c_i$'s above will be $0$, making $\det(U_c) = 0$. On the other hand, if all the eigenvectors are necessary to represent $v$, the resulting subspace spans $V = \mathbb{R}^n$.
Answer for the field $\mathbb C$:
If $A\in\mathbb C^{n\times n}$ and $v\in\mathbb C^n$, the set $\{v,Av,\ldots,A^{n-1}v\}$ is a basis of $\mathbb C^n$ if and only if for each eigenvalue $\lambda$ of $A$ we have $\dim\ker(A-\lambda I) = 1$ and $v\notin\operatorname{im}(A-\lambda I)$.
Of course, the first condition means that minimal and characteristic polynomial coincide. If you pick a random matrix $A$ and a random vector $v$, then both conditions are satisfied with probability $1$, confirming loup blanc's answer.
Proof. Assume that the set is a basis of $\mathbb C^n$. mechanodroid has already proved in their answer that $\dim\ker(A-\lambda I) = 1$ for every eigenvalue $\lambda$ of $A$. Assume that $v = (A-\lambda I)u$ for some eigenvalue $\lambda$ and some $u\in\mathbb C^n$. Then each of the vectors $A^kv = (A-\lambda I)A^ku$ is contained in $\operatorname{im}(A-\lambda I) \neq \mathbb C^n$ and therefore the set cannot span $\mathbb C^n$. A contradiction.
Assume now that the conditions are true for every eigenvalue $\lambda$ and suppose that the set is linearly dependent. Then there exists a monic polynomial $p$ of degree at most $n-1$ such that $p(A)v = 0$. As mechanodroid pointed out in their answer, we have $p|m_A$ and so $p(z) = \prod_{k=1}^m(z-\lambda_k)^{n_k}$, where the $\lambda_k$ are the distinct eigenvalues of $A$ and $\sum_{k=1}^mn_k < n$ (we also allow $n_k = 0$ here). For each $k$ let $\ell_k$ be the smallest number such that $\ker((A-\lambda_k I)^{\ell_k}) = \ker((A-\lambda_k I)^{\ell_k+1})$. It is well known that $$ \mathbb C^n = \mathcal L_1\oplus\dots\oplus\mathcal L_m, $$ where $\mathcal L_k := \ker((A-\lambda_k I)^{\ell_k})$. So, we may decompose $v$ as $v = \sum_{k=1}^mv_k$ with $v_k\in\mathcal L_k$. Now $p(A)v = 0$ implies that $(A-\lambda_k I)^{n_k}v_k = 0$. By assumption, we have $\dim\mathcal L_k = \ell_k$, so $\sum_{k=1}^m\ell_k = n$. Hence, there exists at least one $k$ such that $n_k < \ell_k$. WLOG, let $n_1 < \ell_1$. Again by assumption, this implies the existence of some $u_1\in\ker((A-\lambda_1)^{n_1+1})$ such that $(A-\lambda_1 I)u_1 = v_1$. Now, let $A_k$ denote the restriction of $A$ to $\mathcal L_k$. Then $A_k$ leaves $\mathcal L_k$ invariant and $A_k - \lambda_1 I$ is a bijective mapping on $\mathcal L_k$ for $k\neq 1$. Therefore, $u_k := (A_k - \lambda_1 I)^{-1}v_k\in\mathcal L_k$ exists for $k\neq 1$. Set $u := \sum_{k=1}^mu_k$. Then $(A-\lambda_1 I)u = \sum_{k=1}^m(A_k-\lambda_1 I)u_k = \sum_{k=1}^mv_k = v$, that is, $v\in\operatorname{im}(A-\lambda_1 I)$, which contradicts our assumption.
