As Simone said, it's usually not essential for each fold to be exactly the same size. It'd be perfectly reasonable to have six folds containing eight records and four containing seven records each. That's probably a better solution than having nine folds of size seven and shoving the excess into the last one.
10-fold cross validation is usually a pretty reasonable choice, but you should be aware that there are a passel of related approaches (see this thread), and some of those might better choices, depending on the particulars of your data set. For example, if your classes are very unbalanced, you may want to consider stratified cross-validation, which tries to distribute the classes evenly across the folds (e.g., if you have 16 examples of class A, they're ideally spread across all 10 folds, not lumped together into fold #1 and #2).
Some other schemes, like 5x2 CV, also have relatively nice properties if you're doing inference on the cross-validation results.