Gradient Descent is an optimization algorithm used for minimizing the cost function in machine learning and deep learning models. It iteratively adjusts the model parameters by moving them in the direction opposite to the gradient of the cost function with respect to the parameters. This process continues until the cost function reaches a minimum, indicating the model parameters have been optimized.