The gradient method is a mathematical optimization technique used to find the minimum of a function by iteratively moving in the direction of the steepest descent of the function. It is commonly used in machine learning and numerical optimization to optimize models and algorithms. The method calculates the gradient of the function (the vector of partial derivatives with respect to each variable) at a given point and then moves in the direction opposite to the gradient to find the local minimum. By repeatedly updating the parameters in the direction of the negative gradient, the algorithm eventually converges to the optimal solution. The gradient method is a powerful tool for optimizing complex functions and is widely used in various fields such as engineering, economics, and computer science.