An overfitted model may score almost perfectly on training examples yet fail in real use. Holding out validation and test data reveals the problem. Remedies include more data, regularization such as dropout, simpler models and stopping training earlier.
Benchmarks can be “overfitted” too, when models are tuned to them or their questions leak into training data.