def METHOD_NAME(cv_results):
    """Define the strategy to select the best estimator.
    The strategy defined here is to filter-out all results below a precision threshold
    of 0.98, rank the remaining by recall and keep all models with one standard
    deviation of the best by recall. Once these models are selected, we can select the
    fastest model to predict.
    Parameters
    ----------
    cv_results : dict of numpy (masked) ndarrays
        CV results as returned by the `GridSearchCV`.
    Returns
    -------
    best_index : int
        The index of the best estimator as it appears in `cv_results`.
    """
    # print the info about the grid-search for the different scores
    precision_threshold = 0.98
    cv_results_ = pd.DataFrame(cv_results)
    print("All grid-search results:")
    print_dataframe(cv_results_)
    # Filter-out all results below the threshold
    high_precision_cv_results = cv_results_[
        cv_results_["mean_test_precision"] > precision_threshold
    ]
    print(f"Models with a precision higher than {precision_threshold}:")
    print_dataframe(high_precision_cv_results)
    high_precision_cv_results = high_precision_cv_results[
        [
            "mean_score_time",
            "mean_test_recall",
            "std_test_recall",
            "mean_test_precision",
            "std_test_precision",
            "rank_test_recall",
            "rank_test_precision",
            "params",
        ]
    ]
    # Select the most performant models in terms of recall
    # (within 1 sigma from the best)
    best_recall_std = high_precision_cv_results["mean_test_recall"].std()
    best_recall = high_precision_cv_results["mean_test_recall"].max()
    best_recall_threshold = best_recall - best_recall_std
    high_recall_cv_results = high_precision_cv_results[
        high_precision_cv_results["mean_test_recall"] > best_recall_threshold
    ]
    print(
        "Out of the previously selected high precision models, we keep all the\n"
        "the models within one standard deviation of the highest recall model:"
    )
    print_dataframe(high_recall_cv_results)
    # From the best candidates, select the fastest model to predict
    fastest_top_recall_high_precision_index = high_recall_cv_results[
        "mean_score_time"
    ].idxmin()
    print(
        "\nThe selected final model is the fastest to predict out of the previously\n"
        "selected subset of best models based on precision and recall.\n"
        "Its scoring time is:\n\n"
        f"{high_recall_cv_results.loc[fastest_top_recall_high_precision_index]}"
    )
    return fastest_top_recall_high_precision_index