It's not necessarily a problem that you didn't formulate hypotheses before running the study, but you may be doing a post-hoc analysis, which would be relevant. Also consider whether what your tests mean.
I feel the need to point out that the population is sort of bizarre in this situation. If I understand your study correctly, you have 10 error rates for each model. If you were just interested in the performance for this particular partitioning scheme, you would not need to use statistical tests; these 10 error rates would be the population.
The entire corpus would interest you more. There are (N choose N/10) ways you could partition the data into 90% training set and 10% test set, and you could run the models on a random sample of these partitioning schemes. It seems that this approach is called repeated random sub-sampling validation.
Differences between models
If I understand your dataset, the tests that I am about to describe may not really be valid because the 10 error rates are sort of dependent on each other; they are all taken from the same partitioning scheme.
But here we go anyway! I assume that you are trying to see whether any of the models perform significantly differently from than the others. This is a valid hypothesis, but you'll need to use something like ANOVA because you have more than two models.
On the other hand, if you are just trying to tell the difference between two models because they are the two best models, you have to account for how you decided to compare these two after the fact. Look at post-hoc tests and p-value adjustments.