Gradient-based optimization is a technique used to find the minimum or maximum of a function by iteratively moving in the direction of the steepest descent or ascent, which is determined by the gradient. It's widely used in machine learning and deep learning for training models, as it efficiently handles high-dimensional spaces and non-convex functions.