Model Evaluation is a very important part in any analysis to answer the following questions,
How well does the model fit the data?, Which predictors are most important?, Are the predictions accurate?
So the following are the criterion to access the model performance,

Akaike Information Criteria (AIC): In simple terms, AIC estimates the relative amount of information lost by a given model. So the less information lost the higher the quality of the model. Therefore, we always prefer models with minimum AIC.

Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/ created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC (area under curve). Higher the area under the curve, better the prediction power of the model.

Confusion Matrix: In order to find out how well the model does in predicting the target variable, we use a confusion matrix/ classification rate. It is nothing but a tabular representation of actual Vs predicted values which helps us to find the accuracy of the model.