Gradient descent is a fundamental optimization algorithm widely used in machine learning and statistics. Its primary purpose is to minimize a function by iteratively moving towards the steepest descent, as indicated by the negative gradient. This process involves adjusting model parameters, such as weights in neural networks or coefficients in linear regression, to reduce the cost function. By starting at an initial point, gradient descent recalculates the gradient at each step, guiding the model towards a local minimum. This iterative approach is crucial for training models effectively and efficiently, making it a cornerstone of modern machine learning techniques.