In reply to Kamalini.

For comparison, I suggest using a goodness-of-fit statistic that you can compute for each model. The simplest would be the mean squared error (MSE) or root-MSE, but you could also use the AIC, AICC, BIC, etc. Some researchers prefer cross-validation because that reduces the bias of evaluating the model on the same data that were used to fit it.