Adding extra variables to XGboost model is worsening the train and test accuracy

I am fitting a multi class model using Xgboost. I am getting an accuracy of 96% on Train and 95% on test. I am using the 80-20 train/test split.
However, when I am adding two new features , the accuracy drops down to 92% for train and 89% for test.

Doesnt XGBoost:

1. Pick the most important variable that can be used to split a node and leave out the rest?
2. Handle multi colinearity?

I have not used cross validation. Could it be that I am still overfitting the data ?

This is the code I used

from sklearn.model_selection import train_test_split
df_new_train, df_new_test, y_train, y_test = train_test_split(df, labels2, test_size = 0.2)

dtrain = xgb.DMatrix(df_new_train, label=y_train)
dtest = xgb.DMatrix(df_new_test, label=y_test)

param = {
        'max_depth': 10,
        'early_stopping_rounds': 10,
        'eta': 0.01,
        'subsample': 0.6,
         'colsample_bytree': 0.5,
        #'alpha': 0.5,x`
        #'lambda': 0.5,
        'gamma': 10,
        'min_child_weight': 1,
        'watchlist': [(dtrain, 'train'), (dtest, 'valid')],
        'objective': 'multi:softprob',  # error evaluation for multiclass training
        'num_class': 4}  # the number of classes that exist in this datset
num_round = 1500

bst = xgb.train(param, dtrain,  num_round)

preds = bst.predict(dtest)
preds_train = bst.predict(dtrain)



best_preds_train = np.asarray([np.argmax(line) for line in preds_train])

best_preds = np.asarray([np.argmax(line) for line in preds])


print(classification_report(y_test,best_preds,target_names=label_encoder.classes_ )) 
```