One way to decide which model fits best to your data, is to you use the Mean Squared Error (MSE). The model with lowest MSE can be considered the best fitting model. However, there is more to it. For example, how many parameters more does the better fitting model have compared to the other models. You would want the best fitting model with the fewest parameters.
Also MSE is just one Metric to measure the goodness of fit. Generally, speaking OLS will provide the lowest MSE. Hence the name. However, one can consider a situation, where outliers have a large effect on the estimation of the OSL. You could, for example, estimate your model only on parts of the data and then evaluate the model on the left over data. This could evaluate how well your model works for unseen data.