Statistical models are in observational clinical research primarily used for developing algorithms for individual prediction and for estimating average effects of treatments or of exposure to hazardous agents, and, which is confusing for many authors, these two modelling purposes require different methodological approaches.
While the best prediction model is the model that predicts best (whether or not the parameter estimates are biased is irrelevant) and this is evaluated using the area under the ROC-curve, the best explanatory model is the one with the least biased parameter estimates (prediction accuracy is irrelevant). This requiers considerations regarding cause-effect relationships. For example, confounders are included in the model to reduce confounding bias, but including a factor on the pathway between cause and effect would be a mistake because this would induce adjustment bias.
It is usually wise to avoid presenting risk estimates from prediction models and predictions from explanatory models.