Gradient Descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. It is commonly used in machine learning and deep learning to optimize the parameters of models by reducing the cost function. The algorithm starts with an initial set of parameters and updates them by taking steps proportional to the negative of the gradient of the cost function. Variants of gradient descent include batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. Gradient descent helps in finding the optimal parameters that minimize the error in predictions.