Gradient-based methods are optimization algorithms that use the gradient of the objective function to iteratively adjust parameters, aiming to find a local minimum or maximum. These methods are foundational in machine learning and deep learning, powering techniques like backpropagation to efficiently train models by minimizing error functions.