Model selection is an important aspect of ensemble learning. In bagging, the focus is on selecting models that reduce variance and improve stability. In boosting, the focus is on selecting models that reduce bias and improve the performance of weak learners. In stacking, the focus is on combining models that have strong predictive performance, often using a meta-model to integrate diverse predictions. Therefore, careful model selection is essential for optimizing the performance of ensemble methods.