docs: def validation_curve(model, X, y, param_name, param_range, ax=None, logx=False,
                       groups=None, cv=None, scoring=None, n_jobs=1,
                       pre_dispatch="all", **kwargs):
      """
      Displays a validation curve for the specified param and values, plotting
      both the train and cross-validated test scores. The validation curve is a
      visual, single-parameter grid search used to tune a model to find the best
      balance between error due to bias and error due to variance.
  
      This helper function is a wrapper to use the ValidationCurve in a fast,
      visual 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.
  
      param_name : string
          Name of the parameter that will be varied.
  
      param_range : array-like, shape (n_values,)
          The values of the parameter that will be evaluated.
  
      ax : matplotlib.Axes object, optional
          The axes object to plot the figure on.
  
      logx : boolean, optional
          If True, plots the x-axis with a logarithmic scale.
  
      groups : array-like, with shape (n_samples,)
          Optional group labels for the samples used while splitting the dataset
          into train/test sets.
  
      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 <http://scikit-learn.org/stable/modules/cross_validation.html>`_
          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 model evaluation
          documentation for names of possible metrics.
  
      n_jobs : integer, optional
          Number of jobs to run in parallel (default 1).
  
      pre_dispatch : integer or string, optional
          Number of predispatched jobs for parallel execution (default is
          all). The option can reduce the allocated memory. The string can
          be an expression like '2*n_jobs'.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers. These arguments are
          also passed to the `poof()` method, e.g. can pass a path to save the
          figure to.
  
      Returns
      -------
      ax : matplotlib.Axes
          The axes object that the validation curves were drawn on.
      """
  
      # Initialize the visualizer
      oz = ValidationCurve(
          model, param_name, param_range, ax=ax, logx=logx, groups=groups,
          cv=cv, scoring=scoring, n_jobs=n_jobs, pre_dispatch=pre_dispatch
      )
  
      # Fit and poof the visualizer
      oz.fit(X, y)
      oz.poof(**kwargs)
      return oz.ax