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 towards the steepest descent, as indicated by the negative gradient. This process involves updating model parameters, such as coefficients in linear regression or weights in neural networks, to reduce the cost function. By starting at an initial point, gradient descent recalculates the gradient at each step, allowing the algorithm to converge towards a local minimum. Understanding gradient descent is crucial for effectively training machine learning models and improving their performance.