Boosting is an ensemble learning technique designed to improve the accuracy of machine learning models by combining multiple weak learners into a single strong learner. It works iteratively, where each new model is trained to correct the errors made by the previous ones. This process helps in reducing both bias and variance, making boosting particularly effective for a variety of supervised learning tasks. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost, which have gained widespread use in industry and competitive data science due to their high performance and ease of implementation.