The Ordinary Least Squares (OLS) regression is a statistical method used to estimate the relationship between a dependent variable and one or more independent variables. In the context of the question, the dependent variable is the stock's return, and the independent variable is the return on the S&P 500 Index. The OLS regression aims to find the best-fitting line that minimizes the sum of squared differences between the actual observed returns (stock's return) and the estimated returns predicted by the regression model.

The correct answer, as stated in the file content, is D: "Minimize the sum of squared differences between the actual and estimated stock returns." This is because the primary objective of the OLS procedure is to minimize the overall error between the observed and predicted values, which is quantified by the sum of squared differences. This metric is known as the residual sum of squares (RSS), and minimizing it ensures that the estimated regression line closely fits the observed data points, leading to a more accurate and reliable model for predicting stock returns based on the S&P 500 Index returns.