One possible approach, instead of using OneVsRestClassifier which is for multi-class tasks, is to use MultiOutputClassifier from the sklearn.multioutput module.
Below is a small reproducible sample code with the number of input features and target outputs requested by the OP
import xgboost as xgb
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputClassifier
from sklearn.metrics import accuracy_score
# create sample dataset
X, y = make_multilabel_classification(n_samples=3000, n_features=45, n_classes=20, n_labels=1,
allow_unlabeled=False, random_state=42)
# split dataset into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# create XGBoost instance with default hyper-parameters
xgb_estimator = xgb.XGBClassifier(objective="binary:logistic")
# create MultiOutputClassifier instance with XGBoost model inside
multilabel_model = MultiOutputClassifier(xgb_estimator)
# fit the model
multilabel_model.fit(X_train, y_train)
# evaluate on test data
print('Accuracy on test data: {:.1f}%'.format(accuracy_score(y_test, multilabel_model.predict(X_test))*100))