The least squares method is a mathematical technique used to find the best-fitting line or curve for a set of data points. It works by minimizing the sum of the squares of the differences (errors) between the actual data points and the predicted values on the line or curve. Essentially, it adjusts the line or curve so that it is as close as possible to all the points, ensuring that the overall error is as small as possible. This method is commonly used in regression analysis to create models that can predict future values based on existing data.