Quasi-Newton methods are iterative optimization algorithms used to find local maxima or minima of functions, improving upon the classic Newton's method by approximating the Hessian matrix instead of computing it directly. These methods strike a balance between computational efficiency and convergence speed, making them suitable for large-scale optimization problems.