The segmented least squares method is a way to fit a line to a set of points with minimal error. Instead of trying to fit one line to all the points, the data is split into segments, and a line is fitted to each segment. The goal is to minimize the total error across all segments while using the fewest number of segments possible. This approach allows for a more accurate approximation of the data, especially when fitting a single line with low error is not feasible.