Boosting is a machine learning technique that combines multiple weak models (usually decision trees) to create a stronger, more accurate model. The models are trained sequentially, with each new model correcting the errors made by the previous ones. Boosting algorithms, such as AdaBoost and Gradient Boosting, are highly effective in improving model accuracy, especially for tasks like classification and regression.