with
    signature ``scorer(estimator, X, y)`` which should return only a single value.

    Similar to :func:`cross_validate`
    but only a single metric is permitted.

    If `None`, the estimator's default scorer (if available) is used.

cv : int, cross-validation generator or an iterable, default=None
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:

    - `None`, to use the default 5-fold cross validation,
    - int, to specify the number of folds in a `(Stratified)KFold`,
    - :term:`CV splitter`,
    - An iterable that generates (train, test) splits as arrays of indices.

    For `int`/`None` inputs, if the estimator is a classifier and `y` is
    either binary or multiclass, :class:`StratifiedKFold` is used. In all
    other cases, :class:`KFold` is used. These splitters are instantiated
    with `shuffle=False` so the splits will be the same across calls.

    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.

n_jobs : int, default=None
    Number of jobs to run in parallel. Training the estimator and computing
    the score are parallelized over the cross-validation splits.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.

verbose : int, default=0
    The verbosity level.

params : dict, default=None
    Parameters to pass to the underlying estimator's ``fit``, the scorer,
    and the CV splitter.

    .. versionadded:: 1.4

pre_dispatch : int or str, default='2*n_jobs'
    Controls the number of jobs that get dispatched during parallel
    execution. Reducing this number can be useful to avoid an
    explosion of memory consumption when more jobs get dispatched
    than CPUs can process. This parameter can be:

    - ``None``, in which case all the jobs are immediately created and spawned. Use
      this for lightweight and fast-running jobs, to avoid delays due to on-demand
      spawning of the jobs
    - An int, giving the exact number of total jobs that are spawned
    - A str, giving an expression as a function of n_jobs, as in '2*n_jobs'

error_score : 'raise' or numeric, default=np.nan
    Value to assign to the score if an error occurs in estimator fitting.
    If set to 'raise', the error is raised.
    If a numeric value is given, FitFailedWarning is raised.

    .. versionadded:: 0.20

Returns
-------
scores : ndarray of float of shape=(len(list(cv)),)
    Array of scores of the estimator for each run of the cross validation.

See Also
--------
cross_validate : To run cross-validation on multiple metrics and also to
    return train scores, fit times and score times.

cross_val_predict : Get predictions from each split of cross-validation for
    diagnostic purposes.

sklearn.metrics.make_scorer : Make a scorer from a performance metric or
    loss function.

Examples
--------
>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()
>>> print(cross_val_score(lasso, X, y, cv=3))
[0.3315057  0.08022103 0.03531816]
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