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U-Net, conceived in 2015, is making a resurgence in medical semantic segmentation tasks. This comeback is largely thanks to the excellent performance of nnU-Net in recent competitions. nnU-Net generalizes well, as proven by its first-place finish in the Medical Segmentation Decathalon. Notably, nnU-Net focuses on the training process rather than algorithmic improvements, and can often beat more complex algorithms. This paper shows the results of applying nnU-Net to the KiTS19 Kidney Segmentation Grand Challenge. Each of the 5 cross-validation training folds achieves good results, with scores nearing or exceeding 0.9 after approximately 500 epochs per fold.