The logistic regression model is frequently used in many practical applications to fit a binary response. Model specification depends upon a number of issues including response selection, link specification, and the choice of predictors. Model evaluation includes model selection, predictive ability, and goodness-of-fit. As a result, the art of logistic regression modeling involves many choices and multiple criteria for the data modeler to consider. Particular emphasis will be given to a thorough review of the model selection procedures and the goodness-of-fit testing. In logistic regression, goodness-of-fit assessments sometimes can be challenging, depending on the covariates in the model and the number of covariate patterns. Goodness-of-fit tests can involve chi-square based tests, raw residuals, and transformed residuals. We detail these approaches for assessing the quality of logistic regression models.