Acceptable variation in accuracy of each k fold when using K-Fold Cross Validation? I have a relatively small dataset consisting of 1432 samples.

I have trained a Random Forest Classifier and performed KFold CV. The results of running 10 Fold CV are as follows:

=== 10 Fold Cross Validation Scores ===

CVFold 1 = 90.2%
CVFold 2 = 87.6%
CVFold 3 = 86.7%
CVFold 4 = 86.7%
CVFold 5 = 83.9%
CVFold 6 = 75.8%
CVFold 7 = 87.2%
CVFold 8 = 82.8%
CVFold 9 = 86.1%
CVFold 10 = 89.3%

Mean Cross Validation Score:  85.6%


I am just not sure how to explain why there are such high variances between some of the folds, i.e from 75.8% in fold 6 to 90.2% in fold 1.

My understanding is that it is simply that the classifier found the samples in the 1st fold (90%) easier to classify than it did in fold 6 (75%) but I'm actually not entirely sure if that is the case. 

I understand that each case is different but is it common to have such variances? And is it acceptable?

Edit: some details regarding my data

I have 5 classes, which are imbalanced:

class 1 - 5%

class 2 - 10%

class 3 - 15%

class 4 - 60%

class 5 - 10%

I am using SMOTE to balance the classes.
It's indeed a quite large variation, but nothing alarming since 9 out of 10 folds are within the range 0.8 to 0.9. 

There are many possible factors: yes a fold can be easier than another one by chance, but it also indicates that the training process is a bit unstable. Increasing the number of instances and/or reducing the number of features often helps reducing the variation.