Overfitting
Also: overfit, over-fitting
The model has learned patterns specific to the training sample rather than the underlying relationship, shown by low training error but much higher validation or test error. It is the high-variance side of the bias-variance tradeoff.
Example
A decision tree grown with no depth limit can put each training point in its own leaf: 100% training accuracy, but perhaps only 70% on held-out data.