Gradient Boosting is a powerful Machine Learning technique for building predictive models. It sequentially combines many simple, weak prediction models, typically decision trees, where each new model corrects the errors of its predecessors by following a Gradient in the loss function. This iterative refinement makes it highly effective for both classification and regression tasks.