mRMR provides minimal-optimal feature selection, finding the smallest relevant subset of features for a given ML task. It supports multiple backends including Pandas, Polars, Spark, and Google BigQuery for large-scale feature selection. The algorithm optimizes for both relevance to the target variable and redundancy between selected features, making it efficient for regular ML workflows where feature selection must be performed automatically and frequently.