RANSAC, which stands for RANdom SAmple Consensus, is an iterative method used in statistics and computer vision to estimate parameters of a mathematical model from a dataset containing outliers. It works by randomly selecting a subset of data points to fit a model, then determining how many points from the entire dataset agree with this model. This process is repeated multiple times to find the best-fitting model with the highest consensus. The main advantage of RANSAC is its robustness against outliers, making it particularly useful in applications like image processing, 3D reconstruction, and robotics. By focusing on inliers, RANSAC can provide accurate model estimates even when a significant portion of the data is corrupted or noisy.