Trust-region methods are iterative optimization techniques that restrict the step size by defining a region around the current point where a model is trusted to be an accurate representation of the objective function. These methods adjust the size of the region dynamically based on the agreement between the model and the actual function, ensuring robust convergence even for non-linear or ill-conditioned problems.