Ensemble methods combine multiple machine learning models to create a more powerful predictor than any individual model alone. By leveraging the wisdom of crowds principle, ensembles reduce both variance (through averaging or voting) and bias (through sequential error correction), making them the backbone of winning solutions in data science competitions and production systems. The key to success lies in model diversity—whether achieved through different training subsets (bagging), sequential focus on errors (boosting), or heterogeneous model combinations (stacking)—as diverse models make different mistakes, allowing the ensemble to compensate for individual weaknesses and achieve superior generalization.