Imbalances in features do have an impact on some ML algorithms (typically, the more your algorithm looks like an ensemble method -e.g. random forests-, the less it will be impacted by feature imbalances).
I wrote a blog post a short while ago showing an example of how you could address feature imbalances using a reweighting method (I used it originally to improve the results of a soccer prediction algorithm for which feature imbalances had a noticeable impact on the predicted probabilities of one of the classes).
Cross-validation will work the same: if the distribution of classes and features have a high correlation, then the cross-validation estimate will be affected. There are a lot of possible solutions.
A quick one would be using reweighting observations just like in the blog post I mentioned (could be very efficient for example if you know exactly which features have a high impact on the performance of the algorithm).
Other solutions come from subsampling and bootstrap literature (see for example Kim, 2009 or Bengio & Grandvalet, 2005).
Bengio, Y., & Grandvalet, Y. (2005). Bias in estimating the variance of k-fold cross-validation. Statistical modeling and analysis for complex data problems, 75–95.
Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis, 53(11), 3735–3745. doi:10.1016/j.csda.2009.04.009