The state-of-the-art models for fingerprint enhancement are sophisticated deep neural network architectures that eliminate noise from fingerprints by generating fingerprints image with improved ridge-valley clarity. However, these models perform fingerprint enhancement like a black box and do not specify whether a model is expected to generate an erroneously enhanced fingerprint image. Uncertainty estimation is a standard technique to interpret deep models. Generally, uncertainty in a deep model arises because of uncertainty in parameters of the model (termed as model uncertainty) or noise present in the data (termed as data uncertainty). Recent works showcase the usefulness of uncertainty estimation to interpret fingerprint preprocessing models. Motivated by these works, this chapter presents a detailed analysis of the usefulness of estimating model uncertainty and data uncertainty of fingerprint enhancement models. Furthermore, we also study the generalization ability of both these uncertainties on fingerprint ROI segmentation. A detailed analysis of predicted uncertainties presents insights into the characteristics learnt by each of these uncertainties. Extensive experiments on several challenging fingerprint databases demonstrate the significance of estimating the uncertainty of fingerprint enhancement models.