Boosting is a machine learning technique that combines multiple weak learners to create a strong learner. The algorithm works by iteratively training a series of models, each one focusing on the mistakes made by the previous models in the series. This process allows the final model to correct and improve upon the errors made by the individual weak learners, resulting in a highly accurate and robust predictive model. Boosting is commonly used in classification and regression tasks and has been shown to outperform other machine learning algorithms in many scenarios.