Ensemble methods involve the combination of multiple machine learning models to improve predictive performance. These methods leverage the strength of diverse models by aggregating their predictions to make more accurate and robust decisions. Ensemble methods include techniques such as bagging (bootstrap aggregating), boosting, and stacking. These algorithms have been shown to be highly effective in improving the accuracy and generalization of predictive models in various applications, including classification, regression, and anomaly detection.