Ensemble models are widely used in data science due to their ability to enhance predictive performance, reduce overfitting, handle complex problems, and provide more robust predictions. They are particularly effective when individual models in the ensemble are diverse and have complementary strengths. However, ensemble models can be computationally expensive, require more training data, and may be more challenging to interpret compared to individual models. Proper selection, tuning, and evaluation of ensemble models are essential for achieving the best results.