Cross-validation: K-fold vs Repeated random sub-sampling
If you have an adequate number of samples and want to use all the data, then k-fold cross-validation is the way to go. Having ~1,500 seems like a lot but whether it is adequate for k-fold cross-validation also depends on the dimensionality of the data (number of attributes and number of attribute values). For example, if each observation has 100 attributes, then 1,500 observations is low.

Another potential downside to k-fold cross-validation is the possibility of a single, extreme outlier skewing the results. For example, if you have one extreme outlier that can heavily bias your classifier, then in a 10-fold cross-validation, 9 of the 10 partitions will be affected (though for random forests, I don't think you would have that problem).

Random subsampling (e.g., bootstrap sampling) is preferable when you are either undersampled or when you have the situation above, where you don't want each observation to appear in k-1 folds.