Stacking, or stacked generalization, is an ensemble learning technique in machine learning that combines multiple base models to improve predictive performance. By leveraging the strengths of various algorithms, stacking creates a meta-model that learns to make predictions based on the outputs of these base models. This approach typically involves training several models on the same dataset and then using their predictions as input features for a final model, often referred to as the meta-model. Stacking can lead to enhanced accuracy and robustness, making it a popular choice for complex predictive tasks in data science and machine learning applications.