Boosting algorithms are a type of machine learning technique designed to improve the accuracy of predictive models. They work by combining multiple weak learners, which are models that perform slightly better than random guessing, into a single strong learner. This process involves training each model sequentially, where each new model focuses on correcting the errors made by the previous ones. One popular boosting algorithm is AdaBoost, which adjusts the weights of incorrectly classified instances to emphasize their importance in subsequent models. Other well-known boosting methods include Gradient Boosting and XGBoost, which enhance performance and efficiency, making them widely used in various data science applications.