Quote from walterjennings:
This is a common misconception. Curve fitting leads to a great predictor if you are trying to predict the 'curve' you are fitting to.
Example: Predicting whether a fruit is an apple or orange based on color. System if color == orange => then not apple. Would be considered a highly curve fitted system to the given sample set (since it is technically possible to get an orange apple). Though in reality it offers a very good predictor.
I think what you are worrying about is known as over fitting. Which is where you optimize or fit to a sample set beyond the point of useful predictive knowledge, like in the above example, because in the first sample, say (A,A,O,O,O,A,A), your system believes that all future sample sets will be palindromes, which does not offer useful predictive knowledge of the solution space.