Trust region methods are optimization algorithms that iteratively solve a simpler problem within a region around the current estimate, adjusting the region's size based on the accuracy of the approximation. They are particularly effective for nonlinear optimization problems, balancing exploration and exploitation by controlling the step size to ensure convergence and robustness.