Stacking is a machine learning ensemble method that combines multiple individual models to improve predictive performance. In stacking, the predictions of several base models are used as input features for a meta-model, which then generates the final prediction. This approach allows for capturing more complex patterns and relationships in the data, leading to more accurate and robust predictions. Stacking is commonly used in Kaggle competitions and other data science projects to achieve high levels of predictive accuracy.