Diversity among base models is crucial for improving the performance of ensemble methods. In bagging, diversity reduces variance by training multiple models in parallel, leading to more stable predictions. In boosting, diversity helps in reducing bias by training models sequentially, where each subsequent model tries to correct errors made by the previous model. In stacking, diversity is essential for combining predictions from multiple models, often using a meta-model, which can improve overall performance by leveraging diverse models.