Boost in machine learning is an ensemble meta-algorithm that iteratively transforms weak learners into a single, strong predictive model. Each sequential model focuses on correcting the errors made by its predecessors, gradually refining the overall prediction. This process systematically enhances the model's predictive accuracy and robustness.