docs: def cv_scores(model, X, y, ax=None, cv=None, scoring=None, **kwargs):
      """
      Displays cross validation scores as a bar chart and the
      average of the scores as a horizontal line
  
      This helper function is a quick wrapper to utilize the
      CVScores visualizer for one-off analysis.
  
      Parameters
      ----------
  
      model : a scikit-learn estimator
          An object that implements ``fit`` and ``predict``, can be a
          classifier, regressor, or clusterer so long as there is also a valid
          associated scoring metric.
          Note that the object is cloned for each validation.
  
      X : array-like, shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples and
          n_features is the number of features.
  
      y : array-like, shape (n_samples) or (n_samples, n_features), optional
          Target relative to X for classification or regression;
          None for unsupervised learning.
  
      ax : matplotlib.Axes object, optional
          The axes object to plot the figure on.
  
      cv : int, cross-validation generator or an iterable, optional
          Determines the cross-validation splitting strategy.
          Possible inputs for cv are:
  
          - None, to use the default 3-fold cross-validation,
          - integer, to specify the number of folds.
          - An object to be used as a cross-validation generator.
          - An iterable yielding train/test splits.
  
      see the scikit-learn
      `cross-validation guide <https://goo.gl/FS3VU6>`_
      for more information on the possible strategies that can be used here.
  
      scoring : string, callable or None, optional, default: None
          A string or scorer callable object / function with signature
          ``scorer(estimator, X, y)``.
  
          See scikit-learn `cross-validation guide <https://goo.gl/FS3VU6>`_
          for more information on the possible metrics that can be used.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Returns
      -------
      ax : matplotlib.Axes
          The axes object that the validation curves were drawn on.
  
      """
  
      # Initialize the visualizer
      visualizer = CVScores(model, ax=ax, cv=cv, scoring=scoring)
  
      # Fit and poof the visualizer
      visualizer.fit(X, y)
      visualizer.poof(**kwargs)
      return visualizer.ax