Ordinary Least Squares (OLS) is a linear regression method that estimates the parameters of a linear relationship by minimizing the sum of the squares of the differences between observed and predicted values. It assumes linearity, independence, homoscedasticity, and normally distributed errors to produce the best linear unbiased estimator under the Gauss-Markov theorem.