Repository: scikit-learn/scikit-learn
File: sklearn/model_selection/_search.py (Python)

Code before:

        .. versionchanged:: 0.20
            Support for callable added.

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

        For integer/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.

    verbose : int, default=0
        Controls the verbosity of the grid search.

        Higher values increase the amount of information printed during fitting.

        - 0 : no messages are printed;
        - >=1 : summary of the total number of fits;
        - >=2 : computation time for each fold and parameter candidate;
        - >=3 : fold indices and scores;
        - >=10 : parameter candidate indices and START messages before each fit.

    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. This parameter does not affect the refit

Review comment (suggestion):
What about:

```suggestion
        Controls the verbosity of information printed during fitting, with higher
        values yielding more detailed logging.
```

"of the grid search" seems a bit redundant as this is the docstring for gridsearch. We can then get rid of the next sentence.

Code after:

        .. versionchanged:: 0.20
            Support for callable added.

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

        For integer/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.

    verbose : int, default=0
        Controls the verbosity of information printed during fitting, with higher
        values yielding more detailed logging.

        - 0 : no messages are printed;
        - >=1 : summary of the total number of fits;
        - >=2 : computation time for each fold and parameter candidate;
        - >=3 : fold indices and scores;
        - >=10 : parameter candidate indices and START messages before each fit.

    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. This parameter does not affect the refit
        step, which will always raise the error.