<!-- BEGIN COPY / PASTE -->
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.model_selection import GridSearchCV

    # Define the model
    model = DecisionTreeClassifier()

    # Set up the parameter grid
    param_grid = {
        'max_depth': [None, 10, 20, 30],
        'min_samples_split': [2, 10, 20],
        'min_samples_leaf': [1, 5, 10],
        'criterion': ['gini', 'entropy']
    }

    # Use GridSearchCV to find the best parameters
    grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)
    grid_search.fit(X_train, y_train)

    # Best model
    best_model = grid_search.best_estimator_
    <!-- END COPY / PASTE -->