Bagging, or Bootstrap Aggregating, is an ensemble learning technique that improves the stability and accuracy of machine learning algorithms by combining predictions from multiple models trained on different subsets of the training data. It is particularly effective in reducing variance and helping to avoid overfitting, especially with high-variance models like decision trees.