What does very high accuracy on training data and low accuracy on test data indicate?

Correct answer: Overfitting

This indicates overfitting. The model learns the training data too closely, including noise or accidental patterns. Therefore, it achieves high accuracy on training data but performs poorly on new test data. With good generalization, the gap between training and test performance is usually not very large.