egression model with gcv.

Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
    Training data. Will be cast to float64 if necessary.

y : ndarray of shape (n_samples,) or (n_samples, n_targets)
    Target values. Will be cast to float64 if necessary.

sample_weight : float or ndarray of shape (n_samples,), default=None
    Individual weights for each sample. If given a float, every sample
    will have the same weight. Note that the scale of `sample_weight`
    has an impact on the loss; i.e. multiplying all weights by `k`
    is equivalent to setting `alpha / k`.

score_params : dict, default=None
    Parameters to be passed to the underlying scorer.

    .. versionadded:: 1.5
        See :ref:`Metadata Routing User Guide <metadata_routing>` for
        more details.

Returns
-------
self : object
r