- int, to specify the number of folds.
    - :term:`CV splitter`,
    - An iterable yielding (train, test) splits as arrays of indices.

    For int/None inputs, :class:`~sklearn.model_selection.KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

    .. versionchanged:: 0.22
        ``cv`` default value if None changed from 3-fold to 5-fold.

verbose : bool or int, default=False
    Amount of verbosity.

n_jobs : int, default=None
    Number of CPUs to use during the cross validation. Note that this is
    used only if multiple values for l1_ratio are given.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.

random_state : int, RandomState instance, default=None
    The seed of the pseudo random number generator that selects a random
    feature to update. Used when ``selection`` == 'random'.
    Pass an int for reproducible output across multiple function calls.
    See :term:`Glossary <random_state>`.

selection : {'cyclic', 'random'}, default='cyclic'
    If set to 'random', a random coefficient is updated every iteration
    rather than looping over features sequentially by default. This
    (setting to 'random') often leads to significantly faster convergence
    especially when tol is higher than 1e-4.

Attributes
----------
intercept_ : ndarray of shape (n_targets,)
    Independent term in decision function.

coef_ : ndarray of shape (n_targets, n_features)
    Parameter vector (W in the cost function formula).
    Note that ``coef_`` stores the transpose of ``W``, ``W.T``.

alpha_ : float
    The amount of penalization chosen by cross validation.

mse_path_ : ndarray of shape (n_alphas, n_folds)
    Mean square error for the test set on each fold, varying alpha.

alphas_ : ndarray of shape (n_alphas,)
    The grid of alphas used for fitting.

n_iter_ : int
    Number of iterations run by the coordinate descent solver to reach
    the specified tolerance for the optimal alpha.

dual_gap_ : float
    The dual gap at the end of the optimization for the optimal alpha.

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

See Also
--------
MultiTaskElasticNet : Multi-task ElasticNet model trained with L1/L2
    mixed-norm as regularizer.
ElasticNetCV : Elastic net model with best model selection by
    cross-validation.
MultiTaskElasticNetCV : Multi-task L1/L2 ElasticNet with built-in
    cross-validation.

Notes
-----
The algorithm used to fit the model is coordinate descent.

In `fit`, once the best parameter `alpha` is found through
cross-validation, the model is fit again using the entire training set.

To avoid unnecessary memory duplication the `X` and `y` arguments of the
`fit` method should be directly passed as Fortran-contiguous numpy arrays.

Examples
--------
>>> from sklearn.linear_model import MultiTaskLassoCV
>>> from sklearn.datasets import make_regression
>>> from sklearn.metrics import r2_score
>>> X, y = make_regression(n_targets=2, noise=4, random_state=0)
>>> reg = MultiTaskLassoCV(cv=5, random_state=0).fit(X, y)
>>> r2_score(y, reg.predict(X))
0.9994...
>>> reg.alpha_
np.float64(0.5713...)
>>> reg.predict(X[:1,])
array([[153.7971...,  94.9015...]])
rÄ