The test error is the proportion of incorrect predictions the model makes on a separate, unseen dataset (called the test set).
In other words:
It measures how well the model can generalize to new data it has never seen before.
Example:
If your test set has 2,000 images and the model gets 120 wrong, the test error is 6%.