Quoting robust uncertainties on machine learning (ML) model metrics, such as f1-score, precision, recall, etc., from sources of uncertainty such as data sampling, parameter initialization, and target labelling, is typically not done in the field of data science, even though these are essential for the proper interpretation and comparison of ML models. This text shows how to calculate and visualize the impact of one dominant source of uncertainty – the sampling uncertainty of the test dataset – on each point of the Precision-Recall (PR) and Receiver Operating Characteristic (ROC) curves. This is particularly relevant for PR curves, where the joint uncertainty on recall and precision can be large and non-linear, especially at low recall. Four statistical methods to evaluate this uncertainty, both frequentist and Bayesian in origin, are compared in terms of coverage and speed. Of these, Wilks’ method is the winner: it provides (near) correct coverage for samples as small as 10 records, works fine when the precision or recall are close to the edges of zero or one, and can be evaluated quickly for practical use. The presented algorithms are available through a public Python library. We recommend that showing uncertainty bands of PR or ROC curves becomes the norm, and believe our methodology forms a useful and necessary addition to any data scientist’s toolbox.