Ensemble Retention is a scholarly contribution in the field of ensemble learning that refers to a method designed to preserve and leverage high-performing or informative models within an ensemble over iterative training phases, distinguishing it from ensemble initialization or pruning approaches by prioritizing the sustained inclusion of retained components based on performance, diversity, or representational criteria.