Given some observed data Y, you first pick some explanatory variables X_1, X_2 etc
If you pick a linear model to explain the observed Y, then OLS is the best, linear, unbiased and efficient (BLUE) solution using a computer. It will give you all the parameters of your linear model – the b_0, b_1, b_2 etc.
If you feel the relationship isn’t linear, you still can use OLS. As an alternative to a linear model, you could use AR(1) models to explain Y using the X1 X2 etc. You use AR models when you believe there’s strong serial correlation or autocorrelation.
I believe AR models use additional parameters beside the b1, b2 etc. The computation is more efficient than OLS.