Gradient descent is a fundamental optimization algorithm widely used in machine learning and data science. Its primary purpose is to minimize a function by iteratively moving in the direction of the steepest descent, as defined by the negative gradient. This process involves updating model parameters, such as weights in neural networks or coefficients in linear regression, to reduce the cost function. By continuously recalculating the gradient at each step, gradient descent helps find the optimal solution, making it essential for training various machine learning models effectively. Understanding this algorithm is crucial for anyone working in these fields.