Explain the significance of ensemble methods in machine learning and how techniques like bagging and boosting improve model performance.

Ensemble methods enhance machine learning model performance by combining multiple models to produce a more robust and accurate prediction. Bagging (Bootstrap Aggregating) reduces variance by training multiple models on different subsets of the data and averaging their predictions. Boosting improves accuracy by sequentially training models, each focusing on correcting errors made by the previous one. These techniques help mitigate overfitting and improve generalization.