Ensemble methods are techniques in machine learning that combine multiple models to improve overall performance. Instead of relying on a single model, these methods aggregate the predictions of several models, which can lead to more accurate and robust results. Common ensemble methods include bagging, boosting, and stacking. The main idea behind ensemble methods is that different models may capture different patterns in the data. By combining their strengths, the ensemble can reduce errors and enhance predictive power. This approach is particularly useful in complex problems where individual models may struggle to perform well on their own.