There are several ways to explore the model fit. Easiest would be to compare the AIC and BIC -- smaller values indicate a "better" fit. Next, you can examine the R^2 -- higher values are desirable. Finally, you can predict the residuals and run a Q-Q plot to compare their distribution (you can top it with some formal test, say, Jarque-Bera and see which model's residuals have a smaller chi-square statistic). Additionally, I'd consider comparing the standard errors to see which model provides more efficient ones.

On a side note, have you checked if your models satisfy the required assumptions? I am asking because if, say, you have not met the OLS assumptions (at minimum: normality of residuals, lack of heteroskedasticity and multicollinearity) then what is the purpose of comparing its estimates with other estimators.