XgBoost : The least populated class in y has only 1 members, which is too few

Im using Xgboost implementation on sklearn for a kaggle’s competition.
However, im getting this ‘warning’ message :

$ python Script1.py
/home/sky/private/virtualenv15.0.1dev/myVE/local/lib/python2.7/site-packages/sklearn/cross_validation.py:516: 

Warning: The least populated class in y has only 1 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3.
  % (min_labels, self.n_folds)), Warning)

According to another question on stackoverflow :

Check that you have at least 3 samples per class to be able to do StratifiedKFold cross validation with k == 3 (I think this is the default CV used by GridSearchCV for classification)."

And well, i dont have at least 3 samples per class.

So my questions are:

1. what are the alternatives?

2. Why can’t i use cross validation?

3. What can i use instead?

…

param_test1 = {
    'max_depth': range(3, 10, 2),
    'min_child_weight': range(1, 6, 2)
}

grid_search = GridSearchCV(

estimator=
XGBClassifier(
    learning_rate=0.1,
    n_estimators=3000,
    max_depth=15,
    min_child_weight=1,
    gamma=0,
    subsample=0.8,
    colsample_bytree=0.8,
    objective='multi:softmax',
    nthread=42,
    scale_pos_weight=1,
    seed=27),

    param_grid=param_test1, scoring='roc_auc', n_jobs=42, iid=False, cv=None, verbose=1)
...

grid_search.fit(train_x, place_id)

References:

One-shot learning with scikit-learn

Using a support vector classifier with polynomial kernel in scikit-learn