Gradient descent is an optimization algorithm used in machine learning and mathematical optimization to minimize a function by iteratively moving towards the direction of steepest descent of the function. This is done by calculating the gradient of the function at the current point and updating the parameters in the opposite direction of the gradient. Gradient descent is commonly used to train machine learning models by adjusting the parameters of the model in order to minimize a loss function that measures the difference between the predicted and actual values. By following the gradient of the loss function, the algorithm can find the optimal set of parameters that minimizes the error of the model. There are different variations of gradient descent, such as stochastic gradient descent, mini-batch gradient descent, and batch gradient descent, which differ in the amount of data used to calculate the gradient and update the parameters. Gradient descent is a fundamental algorithm in machine learning and optimization and is widely used in various applications.