Recursive Least Squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients of a linear model to minimize the sum of the squares of the differences between the predicted and actual outputs. It is particularly effective for real-time applications where data is continuously streaming and model parameters need to be updated dynamically without reprocessing the entire dataset.